@conference {2107, title = {The Use of Decision Trees for Adaptive Item Selection and Score Estimation}, booktitle = {Annual Conference of the International Association for Computerized Adaptive Testing}, year = {2011}, month = {10/2011}, abstract = {

Conducted post-hoc simulations comparing the relative efficiency, and precision of decision trees (using CHAID and CART) vs. IRT-based CAT.

Conclusions

Decision tree methods were more efficient than CAT

But,...

Conclusions

CAT selects items based on two criteria: Item location relative to current estimate of theta, Item discrimination

Decision Trees select items that best discriminate between groups defined by the total score.

CAT is optimal only when trait level is well estimated.
Findings suggest that combining decision tree followed by CAT item selection may be advantageous.

}, keywords = {adaptive item selection, CAT, decision tree}, author = {Barth B. Riley and Rodney Funk and Michael L. Dennis and Richard D. Lennox and Matthew Finkelman} }